arXiv subjects

Hong Zhao

Publications and source records attributed to Hong Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2021

Localized surface plasmon enhanced InGaN-based light-emitting diodes with polygonal Au/Al nanoparticles

Localized surface plasmons (LSPs) have played a significant role in improving the light emission efficiency of light emitting diodes (LEDs). In this report, polygonal nanoholes have been fabricated in the p-GaN layer of InGaN-based LEDs by using Ni nanoporous film as the etching mask, and then Au/Al metal nanoparticles are embedded in the nanoholes to form the LSP structure. The coupling between the LSP and the LED has been clearly observed. The results show that the light output of the LEDs has been increased by 46% at higher current injection condition, and together with a shift of the gain peak position to the LSP peak resonance energy. As the coupling distance is decreased from 60 nm to 30 nm, the maximum enhancement factor increases to 2.38. The above results indicate that the LSP from the polygonal metal nanoparticles is a kind of very promising structure to enhance the lighting performance of the InGaN-based LEDs.

physics.optics↗

arXiv2020

Modified Stokes-Einstein Relation for Small Brownian Particles

The Stokes-Einstein (SE) relation has been widely applied to quantitatively describe the Brownian motion. Notwithstanding, here we show that even for a simple fluid, the SE relation may not be completely applicable. Namely, although the SE relation could be a good approximation for a large enough Brownian particle, we find that it induces significant error for a smaller Brownian particle, and the error increases with the decrease of the Brownian particle's size, till the SE relation fails completely when the size of Brownian particle is comparable with that of a fluid molecule. The cause is rooted in the fact that the kinetic and the hydrodynamic effects depend on the size of the Brownian particle differently. By excluding the kinetic contribution to the diffusion coefficient, we propose a revised Stokes-Einstein relation and show that it expands significantly the applicable range.

cond-mat.stat-mech↗

arXiv2020

Long-term variations of quasi-trapped and trapped electrons in the inner radiation belt observed by DEMETER and SAMPEX

Electrons in the Earth's radiation belts can be categorized into three populations: precipitating, quasi-trapped and trapped. We use data from the DEMETER and SAMPEX missions and from ground-based neutron monitors (NM) and sunspot observations to investigate the long-term variation of quasi-trapped and trapped sub-MeV electrons on different L shells in the inner belt. DEMETER and SAMPEX measurements span over 17 years and show that at $L \leq 1.14$ the electron flux is anti-correlated with sunspot number, but proportional to the cosmic ray intensity represented by NM count rates, which suggests that electrons at the inner edge of the inner belt are produced by Cosmic Ray Albedo Neutron Decay (CRAND). The solar cycle variation of cosmic rays increased the electron flux at $L \leq 1.14$ by a factor of two from solar maximum at 2001 to solar minimum at 2009. At $L \ge 1.2$, both quasi-trapped and trapped electrons are enhanced during geomagnetic storms and decay to a background level during extended quiet times. At $L>2$, quasi-trapped electrons resemble trapped electrons, with correlation coefficients as high as 0.97, indicating that pitch angle scattering is the dominant process in this region.

physics.space-ph↗

arXiv2020

Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia

We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

physics.med-ph↗

arXiv2019

Nonintegrability and thermalization of one-dimensional diatomic lattices

Nonintegrability is a necessary condition for the thermalization of a generic Hamiltonian system. In practice, the integrability can be broken in various ways. As illustrating examples, we numerically studied the thermalization behaviors of two types of one-dimensional (1D) diatomic chains in the thermodynamic limit. One chain was the diatomic Toda chain whose nonintegrability was introduced by unequal masses. The other chain was the diatomic Fermi-Pasta-Ulam-Tsingou-$β$ chain whose nonintegrability was introduced by quartic nonlinear interaction. We found that these two different methods of destroying the integrability led to qualitatively different routes to thermalization, but the thermalization time, $T_{eq}$, followed the same law; $T_{eq}$ was inversely proportional to the square of the perturbation strength. This law also agreed with the existing results of 1D monatomic lattices. All these results imply that there is a universal law of thermalization that is independent of the method of breaking integrability.

cond-mat.stat-mech↗

arXiv2019

Identifying the magnetospheric driver of STEVE

For the first time, we identify the magnetospheric driver of STEVE, east-west aligned narrow emissions in the subauroral region. In the ionosphere, STEVE is associated with subauroral ion drift (SAID) features of high electron temperature peak, density gradient, and strong westward ion flow. In this study, we present STEVE's magnetospheric driver region at a sharp plasmapause containing: strong tailward quasi-static electric field, kinetic Alfven waves, parallel electron acceleration, perpendicular ion drift. The observed continuous emissions of STEVE are possibly caused by ionospheric electron heating due to heat conduction and/or auroral acceleration process powered by Alfven waves, both driven by the observed equatorial magnetospheric processes. The observed green emissions are likely optical manifestations of electron precipitations associated with wave structures traveling along the plasmapause. The observed SAR arc at lower latitudes likely corresponds to the formation of low-energy plasma inside the plasmapause by Coulomb collisions between ring current ions and plasmaspheric plasma.

physics.space-ph↗

arXiv2019

Universal law of thermalization for one-dimensional perturbed Toda lattices

The Toda lattice is a nonlinear but integrable system. Here we study the thermalization problem in one-dimensional, perturbed Toda lattices in the thermodynamic limit. We show that the thermalization time, $T_{eq}$, follows a universal law; i.e., $T_{eq}\sim ε^{-2}$, where the perturbation strength, $ε$, characterizes the nonlinear perturbations added to the Toda potential. This universal law applies generally to weak nonlinear lattices due to their equivalence to perturbed Toda systems.

cond-mat.stat-mech↗

arXiv2019

A New Strategy in Applying the Learning Machine to Study Phase Transitions

In this Letter, we present a new strategy for applying the learning machine to study phase transitions. We train the learning machine with samples only obtained at a non-critical parameter point, aiming to establish intrinsic correlations between the learning machine and the target system. Then, we find that the accuracy of the learning machine, which is the most important performance index in conventional learning machines, is no longer a key goal of the training in our approach. Instead, relatively low accuracy of identifying unlabeled data category can help to determine the critical point with greater precision, manifesting the singularity around the critical point. It thus provides a robust tool to study the phase transition. The classical ferromagnetic and percolation phase transitions are employed as illustrative examples.

cond-mat.stat-mech↗

arXiv2018

Universality of Energy Equipartition in One-dimensional Lattices

We show that a general one-dimensional (1D) lattice with nonlinear inter-particle interactions can always be thermalized for arbitrarily small nonlinearity in the thermodynamic limit, thus proving equipartition hypothesis in statistical physics for an important class of systems. Particularly, we find that in the lattices of interaction potential $V(x)=x^2/2+λx^n/n$ with $n\geq4$, there is \textit{a universal scaling law} for the thermalization time $T^{eq}$, i.e., $T^{eq}\proptoλ^{-2}ε^{-(n-2)}$, where $ε$ is the energy density. Numerical simulations confirm that it is accurate for an even $n$. A slight correction is needed for an odd $n$, which is due to the Chirikov overlap occurring in the weakly nonlinear regime between extra vibration modes excited by the asymmetry of potential. Based on this scaling law, as well as previous prediction for the case of $n=3$, a universal formula for the thermalization time for a 1D lattice with a general interaction potential is obtained.

cond-mat.stat-mech↗

arXiv2018

Description of Brownian motion including both kinetic and hydrodynamic effects

It is known that a full description of Brownian motion in the entire course of time should incorporate both kinetic and hydrodynamic effects, but a formula accounts for both effects has been established only in three dimension and only for the limiting case when Brownian particles are much bigger and heavier than fluid particles. Nevertheless, for applications, it is important to consider small Brownian particles (sometimes even smaller than fluid particles). In these cases, unfortunately, only formulae in the short (long) time limit considering exclusively kinetic (hydrodynamic) effect are available. Here we derive a general solution to both two- and three-dimensional Brownian motion. Our solution is applicable to a wide spectrum of suspended particles from being smaller than the fluid particles to usual Brownian particles. Our analytical results are well corroborated by the numerical simulations.

cond-mat.stat-mech↗

arXiv2017

Copy the dynamics using a learning machine

Is it possible to generally construct a dynamical system to simulate a black system without recovering the equations of motion of the latter? Here we show that this goal can be approached by a learning machine. Trained by a set of input-output responses or a segment of time series of a black system, a learning machine can be served as a copy system to mimic the dynamics of various black systems. It can not only behave as the black system at the parameter set that the training data are made, but also recur the evolution history of the black system. As a result, the learning machine provides an effective way for prediction, and enables one to probe the global dynamics of a black system. These findings have significance for practical systems whose equations of motion cannot be approached accurately. Examples of copying the dynamics of an artificial neural network, the Lorenz system, and a variable star are given. Our idea paves a possible way towards copy a living brain.

stat.ML↗

arXiv2017

Brownian motion: from kinetics to hydrodynamics

Brownian motion has served as a pilot of studies in diffusion and other transport phenomena for over a century. The foundation of Brownian motion, laid by Einstein, has generally been accepted to be far from being complete since the late 1960s, because it fails to take important hydrodynamic effects into account. The hydrodynamic effects yield a time dependence of the diffusion coefficient, and this extends the ordinary hydrodynamics. However, the time profile of the diffusion coefficient across the kinetic and hydrodynamic regions is still absent, which prohibits a complete description of Brownian motion in the entire course of time. Here we close this gap. We manage to separate the diffusion process into two parts: a kinetic process governed by the kinetics based on molecular chaos approximation and a hydrodynamics process described by linear hydrodynamics. We find the analytical solution of vortex backflow of hydrodynamic modes triggered by a tagged particle. Coupling it to the kinetic process we obtain explicit expressions of the velocity autocorrelation function and the time profile of diffusion coefficient. This leads to an accurate account of both kinetic and hydrodynamic effects. Our theory is applicable for fluid and Brownian particles, even of irregular-shaped objects, in very general environments ranging from dilute gases to dense liquids. The analytical results are in excellent agreement with numerical experiments.

cond-mat.stat-mech↗

arXiv2017

Violation of the virial theorem and generalized equipartition theorem for logarithmic oscillators serving as a thermostat

A logarithmic oscillator has been proposed recently to serve as a thermostat recently since it has a peculiar property of infinite heat capacity according to the virial theorem. In order to examine its feasibility by numerical simulations, a modified logarithmic potential has been applied in previous studies to eliminate the singularity at origin. The role played by the modification has been elucidated in the present study. We argue that the virial theorem is practically violated for the modified log-oscillator illustrated by a linear dependence of kinetic temperature on energy. Furthermore, as far as a thermalized log-oscillator is concerned, the generalized equipartition theorem with respect to the position coordinate is broken if the temperature is higher than a critical temperature. Finally, we show that log-oscillators fail to serve as thermostats for its incapability of maintaining a nonequilibrium steady state even though their energy is appropriately assigned.

cond-mat.stat-mech↗

arXiv2017

A General Theory for Training Learning Machine

Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.

stat.ML↗

arXiv2017

Image Fusion With Cosparse Analysis Operator

The paper addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited robustness in fusion processing. In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms state-of-the-art fusion methods.

cs.CV↗

arXiv2016

Diffusion of particles in simple fluids: A joint theory of kinetics and hydrodynamics

The particle diffusion in a fluid is a classical topic that dates back to more than one century ago. However, a full solution to this issue still lacks. In this work the velocity autocorrelation function and the diffusion constant are derived analytically, and the hydrodynamics effect on the particle diffusion is analyzed in detail. Unlike previous studies, the ring-collision effect is exhaustively considered in our treatment, and the hydrodynamics approach is extended to the whole time range. Large scale molecular dynamics simulations for the hard-disk fluid show that our analytical results are valid up to the density close to the crystallization point.

physics.flu-dyn↗

arXiv2016

Broken ergodicity of right triangular billiard systems

A right triangular billiard system is equivalent to the system of two colliding particles confined in a one-dimensional box. In spite of their seeming simplicity, no definite conclusion has been drawn so far concerning their ergodic properties. To answer this question, we transform the dynamics of the right triangular billiard system to a piecewise map and analytically prove the broken ergodicity. The mechanism leading to the broken ergodicity is discussed, and some numerical evidence corroborating our conclusion is provided.

cond-mat.stat-mech↗

arXiv2016

The hard-disk fluid revisited

The hard-disk model plays a role of touchstone for testing and developing the transport theory. By large scale molecular dynamics simulations of this model, three important autocorrelation functions, and as a result the corresponding transport coefficients, i.e., the diffusion constant, the thermal conductivity and the shear viscosity, are found to deviate significantly from the predictions of the conventional transport theory beyond the dilute limit. To improve the theory, we consider both the kinetic process and the hydrodynamic process in the whole time range, rather than each process in a seperated time scale as the conventional transport theory does. With this consideration, a unified and coherent expression free of any fitting parameters is derived succesfully in the case of the velocity autocorrelation function, and its superiority to the conventional `piecewise' formula is shown. This expression applies to the whole time range and up to moderate densities, and thus bridges the kinetics and hydrodynamics approaches in a self-consistent manner.

cond-mat.stat-mech↗